1- First use “qdap” package to remove stop words and do stemming as follows (note: replace “covid” with whatever you called your dataframe)
covid_dataset = read.csv("C:/ML/Corona_NLP_train.csv", header = TRUE, stringsAsFactors = FALSE)
summary(covid_dataset) UserName ScreenName Location TweetAt
Min. : 3799 Min. :48751 Length:41157 Length:41157
1st Qu.:14088 1st Qu.:59040 Class :character Class :character
Median :24377 Median :69329 Mode :character Mode :character
Mean :24377 Mean :69329
3rd Qu.:34666 3rd Qu.:79618
Max. :44955 Max. :89907
OriginalTweet Sentiment
Length:41157 Length:41157
Class :character Class :character
Mode :character Mode :character
str(covid_dataset)'data.frame': 41157 obs. of 6 variables:
$ UserName : int 3799 3800 3801 3802 3803 3804 3805 3806 3807 3808 ...
$ ScreenName : int 48751 48752 48753 48754 48755 48756 48757 48758 48759 48760 ...
$ Location : chr "London" "UK" "Vagabonds" "" ...
$ TweetAt : chr "16-03-2020" "16-03-2020" "16-03-2020" "16-03-2020" ...
$ OriginalTweet: chr "@MeNyrbie @Phil_Gahan @Chrisitv https://t.co/iFz9FAn2Pa and https://t.co/xX6ghGFzCC and https://t.co/I2NlzdxNo8" "advice Talk to your neighbours family to exchange phone numbers create contact list with phone numbers of neigh"| __truncated__ "Coronavirus Australia: Woolworths to give elderly, disabled dedicated shopping hours amid COVID-19 outbreak htt"| __truncated__ "My food stock is not the only one which is empty...\n\nPLEASE, don't panic, THERE WILL BE ENOUGH FOOD FOR EVERY"| __truncated__ ...
$ Sentiment : chr "Neutral" "Positive" "Positive" "Positive" ...#Sys.setenv(JAVA_HOME="C:/Program Files/Java/jre1.8.0_301") # replace with your Java installation directory
library(rJava)
#remove.packages("rlang")
#install.packages("rlang", version="1.0.6")
install.packages("qdap")WARNING: Rtools is required to build R packages but is not currently installed. Please download and install the appropriate version of Rtools before proceeding:
https://cran.rstudio.com/bin/windows/Rtools/
Installing package into ‘C:/Users/bbopp3/AppData/Local/R/win-library/4.2’
(as ‘lib’ is unspecified)
trying URL 'https://cran.rstudio.com/bin/windows/contrib/4.2/qdap_2.4.3.zip'
Content type 'application/zip' length 3632084 bytes (3.5 MB)
downloaded 3.5 MBpackage ‘qdap’ successfully unpacked and MD5 sums checked
The downloaded binary packages are in
C:\Users\bbopp3\AppData\Local\Temp\RtmpIl9haY\downloaded_packages#library(qdap)
library(rlang)
library(qdap)Loading required package: qdapDictionaries
Loading required package: qdapRegex
Attaching package: ‘qdapRegex’
The following object is masked from ‘package:rlang’:
%|%
Loading required package: qdapTools
Attaching package: ‘qdapTools’
The following object is masked from ‘package:rlang’:
hash
Loading required package: RColorBrewer
Attaching package: ‘qdap’
The following objects are masked from ‘package:base’:
Filter, proportions#install.packages("SnowballC")
library(SnowballC)covid_dataset$OriginalTweet=rm_stopwords(covid_dataset$OriginalTweet, stopwords=tm::stopwords("english"),
separate=FALSE, strip=TRUE)Registered S3 methods overwritten by 'htmltools':
method from
print.html tools:rstudio
print.shiny.tag tools:rstudio
print.shiny.tag.list tools:rstudiocovid_dataset$OriginalTweet=stemmer(covid_dataset$OriginalTweet, warn=FALSE)
2- Randomize the order of rows, use the same seed as you did in assignment 2 so we can compare the two models on the same test dataset.
covid_dataset= covid_dataset[sample(nrow(covid_dataset)), ]
covid_datasetNA3- Similar to assignment2, Convert sentiment into a factor variable with three levels: “positive, “neutral”, and “negative”. Then convert this factor variable to a numeric vector ( similar to how we encoded the labels for adult dataset in the lectures(slide 94))
covid_dataset$Sentiment[covid_dataset$Sentiment == "Extremely Positive"] = "Positive"
covid_dataset$Sentiment[covid_dataset$Sentiment == "Extremely Negative"] = "Negative"
table(covid_dataset$Sentiment)
Negative Neutral Positive
15398 7713 18046 library("mltools")
covid_dataset$Sentiment <- as.numeric(factor(covid_dataset$Sentiment, levels =c("Positive","Negative","Neutral")))-1
covid_dataset$Sentiment [1] 0 0 1 0 1 0 0 2 2 1 1 0 1 0 1 2 2 1 1 0 0 1 0 1 1 0 0 0 1 1 0 0 0 0
[35] 0 0 1 1 1 1 1 0 1 2 1 1 0 0 0 0 1 0 1 0 0 1 0 1 0 0 0 0 0 1 1 2 0 0
[69] 0 1 0 1 0 0 1 0 1 0 1 2 0 2 0 2 0 0 1 0 0 0 0 1 0 2 2 2 1 1 0 1 2 1
[103] 2 2 2 0 2 1 1 0 1 0 0 2 1 1 1 0 0 0 1 0 1 0 0 1 0 2 0 0 1 2 1 2 1 0
[137] 2 2 1 1 0 0 0 2 0 1 1 0 2 1 0 0 0 0 0 1 0 0 0 0 2 1 2 1 2 2 1 1 1 0
[171] 0 0 0 0 0 0 1 1 1 2 2 0 1 1 0 1 0 0 1 0 1 2 1 1 2 0 2 0 0 1 1 1 1 2
[205] 0 2 1 1 1 2 0 1 0 0 0 2 0 0 0 1 0 0 2 2 1 1 0 0 1 0 0 0 0 0 0 0 2 0
[239] 0 2 1 0 2 0 0 1 1 2 0 1 0 0 0 0 2 1 0 1 0 0 0 1 1 0 0 1 1 0 0 2 0 1
[273] 2 0 0 0 0 0 1 0 2 1 1 0 2 1 0 2 0 2 0 0 0 0 1 1 1 0 0 2 1 0 1 0 1 0
[307] 2 1 1 1 1 1 1 0 2 2 0 1 0 1 1 1 2 0 0 0 0 2 0 2 0 1 1 0 1 1 0 1 0 1
[341] 1 0 1 0 0 0 0 0 1 0 2 1 2 1 1 1 1 0 0 2 0 0 0 0 2 1 0 1 0 0 1 0 2 1
[375] 0 0 2 0 0 0 1 0 0 0 2 1 0 2 1 0 2 2 2 0 2 0 1 1 1 0 1 0 2 1 1 1 1 0
[409] 2 1 0 0 0 1 0 0 0 1 0 0 1 2 2 0 0 0 1 1 1 0 0 0 0 0 0 1 2 2 0 1 2 1
[443] 0 0 1 2 2 0 1 0 1 1 0 0 0 1 0 0 2 0 1 2 1 1 0 0 0 1 0 0 1 1 2 2 1 0
[477] 0 0 1 0 1 0 2 0 0 0 0 0 2 1 0 0 1 0 1 2 2 0 0 1 0 1 2 2 2 1 1 0 1 0
[511] 1 1 0 0 1 1 0 0 0 2 1 0 0 1 1 0 1 1 0 0 1 1 0 1 0 0 2 2 1 0 2 2 2 0
[545] 0 0 1 0 0 0 1 0 1 1 1 2 2 2 0 0 1 0 0 0 0 0 0 1 1 2 0 1 1 0 1 0 2 2
[579] 0 0 2 0 2 0 0 1 2 1 1 0 1 2 2 1 0 1 1 1 1 2 0 0 1 0 1 2 0 1 1 1 0 0
[613] 0 0 0 1 2 0 1 1 0 0 0 1 1 0 0 1 1 0 1 0 1 1 1 1 2 0 2 0 0 1 1 1 1 1
[647] 2 1 1 0 2 1 1 0 2 0 1 2 1 1 2 1 1 1 0 0 2 1 0 0 2 2 0 0 2 0 1 0 2 1
[681] 1 1 0 1 1 1 0 1 0 0 1 1 2 0 2 2 0 0 0 1 1 0 2 2 1 2 2 2 2 0 1 1 0 1
[715] 1 0 0 0 1 0 1 1 2 2 0 0 0 0 0 2 2 0 0 0 0 1 2 1 0 1 0 0 1 2 1 0 0 1
[749] 0 2 1 0 1 0 0 2 1 0 0 2 0 1 0 0 0 1 1 0 1 1 0 1 0 2 1 1 1 0 0 1 2 0
[783] 2 1 1 1 0 0 0 0 0 0 0 0 1 1 0 0 1 2 0 0 1 0 1 2 1 2 0 0 2 1 0 0 0 2
[817] 2 0 2 0 1 0 1 1 1 1 0 2 1 1 0 1 0 2 1 1 0 1 1 2 1 2 1 1 1 1 1 0 0 0
[851] 0 2 0 1 1 2 2 1 0 0 1 2 1 1 0 0 0 1 0 1 1 0 1 2 2 1 1 0 1 1 0 0 1 2
[885] 0 0 0 1 0 0 0 0 1 0 1 1 1 0 0 0 1 2 0 1 0 1 0 2 2 2 1 1 1 1 2 1 1 0
[919] 1 0 1 0 1 1 1 1 1 1 0 2 1 2 0 1 0 2 0 0 0 1 2 1 0 0 0 0 1 0 1 0 2 1
[953] 0 2 1 0 0 1 2 1 1 1 2 0 0 1 0 1 2 0 1 1 1 1 2 0 0 0 1 0 1 1 0 2 0 1
[987] 2 1 0 2 0 0 2 2 0 0 1 2 0 2
[ reached getOption("max.print") -- omitted 40157 entries ]4- Spit the data three ways in to train/validation/ and test sets as follows: use the first 26340 rows for training, next 6585 rows for validation, and the last 8232 rows for testing. Make sure that you are using the same test set as you used in assignment 2 so you can compare the ANN model with your naïve Bayes model in assignmentt2.
#install.packages("splitTools")
#install.packages("ranger")
library("splitTools")
library("ranger")
train=list()
valid=list()
test=list()
train= covid_dataset[1:26340,-6]
valid = covid_dataset[26341:32925,-6]
test= covid_dataset[32926:41157,-6]
train_la = as.matrix(covid_dataset[1:26340,6])
valid_la = as.matrix(covid_dataset[26341:32925,6])
test_la = as.matrix(covid_dataset[32926:41157,6])
str(train_la) num [1:26340, 1] 0 0 1 0 1 0 0 2 2 1 ...str(covid_dataset)'data.frame': 41157 obs. of 6 variables:
$ UserName : int 37545 31215 38683 9938 42480 6915 33056 44068 22511 29137 ...
$ ScreenName : int 82497 76167 83635 54890 87432 51867 78008 89020 67463 74089 ...
$ Location : chr "" "New Delhi, India" "Berlin, Germany" "UK" ...
$ TweetAt : chr "08-04-2020" "03-04-2020" "08-04-2020" "19-03-2020" ...
$ OriginalTweet: chr "Petrol diesel price static rd success day amid covid nationwid lockdown key thing know" "Rise explod due onlin shop becom lifelin mani expos onlin like credit card skim" "Women coronavirus symptom post viral recoveri may affect menstrual cycl can confirm experi day mention studi t "| __truncated__ "Joul urg govern help worker uk retail sector econom impact coronavirus continu httpstcov fngxxlff" ...
$ Sentiment : num 0 0 1 0 1 0 0 2 2 1 ...5- Keras has a preprocessing layer, called layer_text_vectorization, this layer creates a documentterm matrix where rows represent tweets and columns represent terms. Use the following code segment to create document-term matrix for your training, validation and test datasets you created above. (Note: replace covid_train, covid_test, and covid_val with the names you gave to your train, test and validation sets):
library("keras")
text_vectorizer <- layer_text_vectorization(output_mode="tf_idf", ngrams =2, max_tokens =5000)
text_vectorizer %>% adapt(train$OriginalTweet)
covid_train_dtm = text_vectorizer(train$OriginalTweet)
covid_val_dtm =text_vectorizer(valid$OriginalTweet)
covid_test_dtm= text_vectorizer(test$OriginalTweet)
Q1. (5 pts) Create an ANN model with two hidden layers to classify tweets into three classes (“Negative”, “Neutral”, and “Positive”). Note: This is a multi-class classification problem so make sure that you are using a correct loss function as well correct number of neurons/units in the final/output layer with correct activation function.
model =keras_model_sequential()
model %>%
layer_dense(units = 64, activation = 'relu') %>%
layer_dense(units = 128, activation = 'relu') %>%
layer_dense(units = 3, activation = 'softmax')
model %>% compile(
optimizer = 'adam',
loss = 'sparse_categorical_crossentropy',
metrics = c('accuracy'))
modelModel: <no summary available, model was not built>
set.seed(111)
model %>% fit(covid_train_dtm,train_la,epochs=30,batch_size=100,validation_data=list(covid_val_dtm,valid_la))Epoch 1/30
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264/264 [==============================] - 3s 10ms/step - loss: 0.7553 - accuracy: 0.6727 - val_loss: 0.6589 - val_accuracy: 0.7468
Epoch 2/30
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264/264 [==============================] - 2s 8ms/step - loss: 0.4895 - accuracy: 0.8182 - val_loss: 0.6123 - val_accuracy: 0.7680
Epoch 3/30
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264/264 [==============================] - 2s 7ms/step - loss: 0.1495 - accuracy: 0.9485 - val_loss: 0.9833 - val_accuracy: 0.7523
Epoch 6/30
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264/264 [==============================] - 2s 8ms/step - loss: 0.0812 - accuracy: 0.9736 - val_loss: 1.1767 - val_accuracy: 0.7511
Epoch 7/30
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264/264 [==============================] - ETA: 0s - loss: 0.0476 - accuracy: 0.9867
264/264 [==============================] - 2s 9ms/step - loss: 0.0476 - accuracy: 0.9867 - val_loss: 1.4338 - val_accuracy: 0.7502
Epoch 8/30
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264/264 [==============================] - 2s 7ms/step - loss: 0.0274 - accuracy: 0.9938 - val_loss: 1.6050 - val_accuracy: 0.7461
Epoch 9/30
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264/264 [==============================] - 2s 7ms/step - loss: 0.0117 - accuracy: 0.9976 - val_loss: 1.7729 - val_accuracy: 0.7456
Epoch 10/30
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264/264 [==============================] - 2s 7ms/step - loss: 0.0086 - accuracy: 0.9982 - val_loss: 1.8403 - val_accuracy: 0.7481
Epoch 11/30
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264/264 [==============================] - 3s 11ms/step - loss: 0.0103 - accuracy: 0.9975 - val_loss: 2.0185 - val_accuracy: 0.7473
Epoch 12/30
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264/264 [==============================] - 2s 9ms/step - loss: 0.0164 - accuracy: 0.9957 - val_loss: 1.9240 - val_accuracy: 0.7364
Epoch 13/30
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264/264 [==============================] - 2s 8ms/step - loss: 0.0352 - accuracy: 0.9883 - val_loss: 2.1553 - val_accuracy: 0.7406
Epoch 14/30
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264/264 [==============================] - 2s 9ms/step - loss: 0.0523 - accuracy: 0.9839 - val_loss: 1.8085 - val_accuracy: 0.7371
Epoch 15/30
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264/264 [==============================] - 2s 8ms/step - loss: 0.0296 - accuracy: 0.9916 - val_loss: 1.9340 - val_accuracy: 0.7379
Epoch 16/30
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264/264 [==============================] - 2s 9ms/step - loss: 0.0113 - accuracy: 0.9967 - val_loss: 2.1624 - val_accuracy: 0.7408
Epoch 17/30
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264/264 [==============================] - 2s 8ms/step - loss: 0.0042 - accuracy: 0.9991 - val_loss: 2.3027 - val_accuracy: 0.7456
Epoch 18/30
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264/264 [==============================] - 2s 8ms/step - loss: 0.0027 - accuracy: 0.9995 - val_loss: 2.4103 - val_accuracy: 0.7426
Epoch 19/30
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264/264 [==============================] - 2s 8ms/step - loss: 0.0020 - accuracy: 0.9994 - val_loss: 2.4933 - val_accuracy: 0.7473
Epoch 20/30
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264/264 [==============================] - 2s 8ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 2.5153 - val_accuracy: 0.7461
Epoch 21/30
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264/264 [==============================] - 2s 9ms/step - loss: 0.0016 - accuracy: 0.9996 - val_loss: 2.5356 - val_accuracy: 0.7476
Epoch 22/30
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264/264 [==============================] - 2s 8ms/step - loss: 0.0021 - accuracy: 0.9995 - val_loss: 2.5619 - val_accuracy: 0.7488
Epoch 23/30
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264/264 [==============================] - 2s 8ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 2.7268 - val_accuracy: 0.7485
Epoch 24/30
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264/264 [==============================] - 2s 8ms/step - loss: 0.0469 - accuracy: 0.9858 - val_loss: 2.1158 - val_accuracy: 0.7362
Epoch 25/30
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264/264 [==============================] - 2s 8ms/step - loss: 0.0241 - accuracy: 0.9926 - val_loss: 2.1956 - val_accuracy: 0.7383
Epoch 26/30
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264/264 [==============================] - 2s 7ms/step - loss: 0.0199 - accuracy: 0.9939 - val_loss: 2.2929 - val_accuracy: 0.7388
Epoch 27/30
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264/264 [==============================] - 2s 8ms/step - loss: 0.0093 - accuracy: 0.9973 - val_loss: 2.3726 - val_accuracy: 0.7426
Epoch 28/30
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264/264 [==============================] - ETA: 0s - loss: 0.0052 - accuracy: 0.9986
264/264 [==============================] - 2s 9ms/step - loss: 0.0052 - accuracy: 0.9986 - val_loss: 2.3846 - val_accuracy: 0.7356
Epoch 29/30
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264/264 [==============================] - 2s 8ms/step - loss: 0.0022 - accuracy: 0.9995 - val_loss: 2.4593 - val_accuracy: 0.7465
Epoch 30/30
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264/264 [==============================] - ETA: 0s - loss: 0.0015 - accuracy: 0.9995
264/264 [==============================] - 2s 8ms/step - loss: 0.0015 - accuracy: 0.9995 - val_loss: 2.5607 - val_accuracy: 0.7380
model %>% evaluate(covid_test_dtm, test_la)
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loss accuracy
2.3952410 0.7459912 predicted_labels = as.numeric(model %>% predict(covid_test_dtm) %>%k_argmax())
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258/258 [==============================] - 0s 2ms/steppredicted_labels[1:30] [1] 0 0 0 0 0 1 0 0 2 0 0 1 1 0 0 1 1 0 1 0 0 2 1 1 1 0 0 1 0 1t = table(test_la, predicted_labels)
t predicted_labels
test_la 0 1 2
0 2760 590 235
1 418 2480 224
2 297 327 901error = (t[1,2]+t[2,1])/sum(t)
error[1] 0.122449Q2. (5 pts) Use “tfruns” package to tune your ANN’s hyper- parameters including the number of nodes in each hidden layer, the batch_size, and learning_rate). Validate each model on the validation set. Answer the following questions:
library(tfruns)
set.seed(123)
set.seed(1)
runs <- tuning_run("C:/ML/covidd.R",
flags = list(
nodes = c(64, 128, 392),
learning_rate = c(0.01, 0.05, 0.001, 0.0001),
batch_size=c(100,200,500,1000),
epochs=c(30,50, 100),
activation=c("relu","sigmoid", "tanh")),
sample = 0.02
)432 total combinations of flags
(sampled to 9 combinations)
Training run 1/9 (flags = list(392, 1e-04, 500, 30, "tanh"))
Using run directory runs/2023-04-06T23-52-26Z
> Flags1 <- list(
+ nodes = 128,
+ batch_size = 100,
+ activation = "relu",
+ learning_rate = 0.01,
+ epochs = 30
+ )
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = Flags1$nodes, activation = Flags1$activation) %>%
+ layer_dense(units = 32, activation = Flags1$activation) %>%
.... [TRUNCATED]
> model %>% compile(optimizer = optimizer_adam(lr = Flags1$learning_rate), loss = 'sparse_categorical_crossentropy', metrics = c('accuracy'))WARNING:absl:`lr` is deprecated, please use `learning_rate` instead, or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
> model %>% fit(
+ covid_train_dtm, train_la, epochs = Flags1$epochs, batch_size = Flags1$batch_size, validation_data=list(covid_val_dtm, valid_la)
.... [TRUNCATED]
Epoch 1/30
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264/264 [==============================] - ETA: 0s - loss: 0.7671 - accuracy: 0.6724
264/264 [==============================] - 5s 15ms/step - loss: 0.7671 - accuracy: 0.6724 - val_loss: 0.6529 - val_accuracy: 0.7481
Epoch 2/30
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264/264 [==============================] - 3s 13ms/step - loss: 0.5004 - accuracy: 0.8132 - val_loss: 0.6408 - val_accuracy: 0.7596
Epoch 3/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.3686 - accuracy: 0.8662 - val_loss: 0.6894 - val_accuracy: 0.7608
Epoch 4/30
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264/264 [==============================] - 4s 13ms/step - loss: 0.2326 - accuracy: 0.9163 - val_loss: 0.8041 - val_accuracy: 0.7566
Epoch 5/30
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264/264 [==============================] - ETA: 0s - loss: 0.1179 - accuracy: 0.9618
264/264 [==============================] - 3s 13ms/step - loss: 0.1179 - accuracy: 0.9618 - val_loss: 0.9882 - val_accuracy: 0.7459
Epoch 6/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0543 - accuracy: 0.9853 - val_loss: 1.1889 - val_accuracy: 0.7393
Epoch 7/30
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264/264 [==============================] - 3s 11ms/step - loss: 0.0281 - accuracy: 0.9934 - val_loss: 1.2916 - val_accuracy: 0.7399
Epoch 8/30
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264/264 [==============================] - 4s 14ms/step - loss: 0.0167 - accuracy: 0.9968 - val_loss: 1.4693 - val_accuracy: 0.7424
Epoch 9/30
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264/264 [==============================] - 4s 15ms/step - loss: 0.0098 - accuracy: 0.9981 - val_loss: 1.4961 - val_accuracy: 0.7376
Epoch 10/30
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264/264 [==============================] - 4s 16ms/step - loss: 0.0070 - accuracy: 0.9987 - val_loss: 1.6823 - val_accuracy: 0.7411
Epoch 11/30
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264/264 [==============================] - 4s 14ms/step - loss: 0.0065 - accuracy: 0.9987 - val_loss: 1.7395 - val_accuracy: 0.7446
Epoch 12/30
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264/264 [==============================] - 4s 14ms/step - loss: 0.0082 - accuracy: 0.9984 - val_loss: 1.8770 - val_accuracy: 0.7212
Epoch 13/30
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264/264 [==============================] - 4s 13ms/step - loss: 0.0189 - accuracy: 0.9949 - val_loss: 1.8102 - val_accuracy: 0.7247
Epoch 14/30
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264/264 [==============================] - 4s 14ms/step - loss: 0.0324 - accuracy: 0.9896 - val_loss: 1.7806 - val_accuracy: 0.7230
Epoch 15/30
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264/264 [==============================] - 4s 14ms/step - loss: 0.0284 - accuracy: 0.9908 - val_loss: 1.8657 - val_accuracy: 0.7218
Epoch 16/30
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264/264 [==============================] - 4s 14ms/step - loss: 0.0170 - accuracy: 0.9955 - val_loss: 2.0034 - val_accuracy: 0.7259
Epoch 17/30
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264/264 [==============================] - 4s 14ms/step - loss: 0.0088 - accuracy: 0.9980 - val_loss: 2.0888 - val_accuracy: 0.7332
Epoch 18/30
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264/264 [==============================] - ETA: 0s - loss: 0.0026 - accuracy: 0.9992
264/264 [==============================] - 4s 14ms/step - loss: 0.0026 - accuracy: 0.9992 - val_loss: 2.1808 - val_accuracy: 0.7298
Epoch 19/30
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264/264 [==============================] - 4s 15ms/step - loss: 0.0032 - accuracy: 0.9993 - val_loss: 2.3151 - val_accuracy: 0.7312
Epoch 20/30
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264/264 [==============================] - 4s 15ms/step - loss: 0.0028 - accuracy: 0.9994 - val_loss: 2.2670 - val_accuracy: 0.7308
Epoch 21/30
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264/264 [==============================] - 4s 14ms/step - loss: 0.0013 - accuracy: 0.9995 - val_loss: 2.3428 - val_accuracy: 0.7336
Epoch 22/30
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264/264 [==============================] - ETA: 0s - loss: 0.0019 - accuracy: 0.9994
264/264 [==============================] - 4s 14ms/step - loss: 0.0019 - accuracy: 0.9994 - val_loss: 2.4469 - val_accuracy: 0.7342
Epoch 23/30
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264/264 [==============================] - 4s 15ms/step - loss: 0.0024 - accuracy: 0.9994 - val_loss: 2.4694 - val_accuracy: 0.7274
Epoch 24/30
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264/264 [==============================] - 4s 14ms/step - loss: 0.0014 - accuracy: 0.9997 - val_loss: 2.5117 - val_accuracy: 0.7349
Epoch 25/30
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264/264 [==============================] - 4s 15ms/step - loss: 0.0048 - accuracy: 0.9985 - val_loss: 2.2030 - val_accuracy: 0.7206
Epoch 26/30
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264/264 [==============================] - 4s 14ms/step - loss: 0.0312 - accuracy: 0.9913 - val_loss: 2.1130 - val_accuracy: 0.7233
Epoch 27/30
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264/264 [==============================] - ETA: 0s - loss: 0.0265 - accuracy: 0.9928
264/264 [==============================] - 4s 14ms/step - loss: 0.0265 - accuracy: 0.9928 - val_loss: 2.1586 - val_accuracy: 0.7224
Epoch 28/30
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264/264 [==============================] - 4s 15ms/step - loss: 0.0141 - accuracy: 0.9962 - val_loss: 2.1856 - val_accuracy: 0.7180
Epoch 29/30
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264/264 [==============================] - ETA: 0s - loss: 0.0056 - accuracy: 0.9985
264/264 [==============================] - 4s 15ms/step - loss: 0.0056 - accuracy: 0.9985 - val_loss: 2.3380 - val_accuracy: 0.7301
Epoch 30/30
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264/264 [==============================] - ETA: 0s - loss: 0.0015 - accuracy: 0.9995
264/264 [==============================] - 4s 14ms/step - loss: 0.0015 - accuracy: 0.9995 - val_loss: 2.4238 - val_accuracy: 0.7312
Run completed: runs/2023-04-06T23-52-26Z
Training run 2/9 (flags = list(128, 1e-04, 200, 30, "sigmoid"))
Using run directory runs/2023-04-06T23-54-18Z
> Flags1 <- list(
+ nodes = 128,
+ batch_size = 100,
+ activation = "relu",
+ learning_rate = 0.01,
+ epochs = 30
+ )
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = Flags1$nodes, activation = Flags1$activation) %>%
+ layer_dense(units = 32, activation = Flags1$activation) %>%
.... [TRUNCATED]
> model %>% compile(optimizer = optimizer_adam(lr = Flags1$learning_rate), loss = 'sparse_categorical_crossentropy', metrics = c('accuracy'))WARNING:absl:`lr` is deprecated, please use `learning_rate` instead, or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
> model %>% fit(
+ covid_train_dtm, train_la, epochs = Flags1$epochs, batch_size = Flags1$batch_size, validation_data=list(covid_val_dtm, valid_la)
.... [TRUNCATED]
Epoch 1/30
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264/264 [==============================] - 5s 15ms/step - loss: 0.7461 - accuracy: 0.6812 - val_loss: 0.6402 - val_accuracy: 0.7546
Epoch 2/30
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264/264 [==============================] - 4s 14ms/step - loss: 0.4812 - accuracy: 0.8211 - val_loss: 0.6387 - val_accuracy: 0.7623
Epoch 3/30
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264/264 [==============================] - ETA: 0s - loss: 0.3438 - accuracy: 0.8754
264/264 [==============================] - 4s 13ms/step - loss: 0.3438 - accuracy: 0.8754 - val_loss: 0.6829 - val_accuracy: 0.7581
Epoch 4/30
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264/264 [==============================] - ETA: 0s - loss: 0.2023 - accuracy: 0.9320
264/264 [==============================] - 3s 12ms/step - loss: 0.2023 - accuracy: 0.9320 - val_loss: 0.7845 - val_accuracy: 0.7575
Epoch 5/30
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264/264 [==============================] - ETA: 0s - loss: 0.1038 - accuracy: 0.9677
264/264 [==============================] - 3s 12ms/step - loss: 0.1038 - accuracy: 0.9677 - val_loss: 0.9799 - val_accuracy: 0.7455
Epoch 6/30
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264/264 [==============================] - 4s 14ms/step - loss: 0.0476 - accuracy: 0.9872 - val_loss: 1.1610 - val_accuracy: 0.7503
Epoch 7/30
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264/264 [==============================] - 4s 14ms/step - loss: 0.0239 - accuracy: 0.9945 - val_loss: 1.4111 - val_accuracy: 0.7458
Epoch 8/30
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264/264 [==============================] - 4s 13ms/step - loss: 0.0161 - accuracy: 0.9967 - val_loss: 1.4810 - val_accuracy: 0.7456
Epoch 9/30
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264/264 [==============================] - 3s 13ms/step - loss: 0.0118 - accuracy: 0.9978 - val_loss: 1.6785 - val_accuracy: 0.7450
Epoch 10/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0087 - accuracy: 0.9981 - val_loss: 1.7186 - val_accuracy: 0.7458
Epoch 11/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0104 - accuracy: 0.9979 - val_loss: 1.6953 - val_accuracy: 0.7449
Epoch 12/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0119 - accuracy: 0.9968 - val_loss: 1.7397 - val_accuracy: 0.7399
Epoch 13/30
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264/264 [==============================] - ETA: 0s - loss: 0.0260 - accuracy: 0.9922
264/264 [==============================] - 3s 11ms/step - loss: 0.0260 - accuracy: 0.9922 - val_loss: 1.8737 - val_accuracy: 0.7370
Epoch 14/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0335 - accuracy: 0.9910 - val_loss: 1.7757 - val_accuracy: 0.7292
Epoch 15/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0217 - accuracy: 0.9937 - val_loss: 1.8764 - val_accuracy: 0.7342
Epoch 16/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0192 - accuracy: 0.9945 - val_loss: 1.8622 - val_accuracy: 0.7329
Epoch 17/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0104 - accuracy: 0.9971 - val_loss: 2.2068 - val_accuracy: 0.7213
Epoch 18/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0070 - accuracy: 0.9985 - val_loss: 2.1084 - val_accuracy: 0.7321
Epoch 19/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0034 - accuracy: 0.9991 - val_loss: 2.2197 - val_accuracy: 0.7417
Epoch 20/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0027 - accuracy: 0.9994 - val_loss: 2.2999 - val_accuracy: 0.7342
Epoch 21/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0030 - accuracy: 0.9992 - val_loss: 2.1946 - val_accuracy: 0.7405
Epoch 22/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0023 - accuracy: 0.9991 - val_loss: 2.3171 - val_accuracy: 0.7341
Epoch 23/30
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264/264 [==============================] - ETA: 0s - loss: 0.0074 - accuracy: 0.9976
264/264 [==============================] - 3s 12ms/step - loss: 0.0074 - accuracy: 0.9976 - val_loss: 2.3408 - val_accuracy: 0.7280
Epoch 24/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0146 - accuracy: 0.9956 - val_loss: 2.2149 - val_accuracy: 0.7268
Epoch 25/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0186 - accuracy: 0.9942 - val_loss: 2.1785 - val_accuracy: 0.7362
Epoch 26/30
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264/264 [==============================] - 3s 11ms/step - loss: 0.0075 - accuracy: 0.9977 - val_loss: 2.5918 - val_accuracy: 0.7365
Epoch 27/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0079 - accuracy: 0.9980 - val_loss: 2.3933 - val_accuracy: 0.7342
Epoch 28/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0072 - accuracy: 0.9981 - val_loss: 2.3636 - val_accuracy: 0.7368
Epoch 29/30
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264/264 [==============================] - 3s 11ms/step - loss: 0.0069 - accuracy: 0.9980 - val_loss: 2.2413 - val_accuracy: 0.7358
Epoch 30/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0074 - accuracy: 0.9984 - val_loss: 2.3264 - val_accuracy: 0.7374
Run completed: runs/2023-04-06T23-54-18Z
Training run 3/9 (flags = list(392, 0.001, 500, 100, "relu"))
Using run directory runs/2023-04-06T23-55-56Z
> Flags1 <- list(
+ nodes = 128,
+ batch_size = 100,
+ activation = "relu",
+ learning_rate = 0.01,
+ epochs = 30
+ )
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = Flags1$nodes, activation = Flags1$activation) %>%
+ layer_dense(units = 32, activation = Flags1$activation) %>%
.... [TRUNCATED]
> model %>% compile(optimizer = optimizer_adam(lr = Flags1$learning_rate), loss = 'sparse_categorical_crossentropy', metrics = c('accuracy'))WARNING:absl:`lr` is deprecated, please use `learning_rate` instead, or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
> model %>% fit(
+ covid_train_dtm, train_la, epochs = Flags1$epochs, batch_size = Flags1$batch_size, validation_data=list(covid_val_dtm, valid_la)
.... [TRUNCATED]
Epoch 1/30
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264/264 [==============================] - 4s 14ms/step - loss: 0.7586 - accuracy: 0.6735 - val_loss: 0.6662 - val_accuracy: 0.7467
Epoch 2/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.5006 - accuracy: 0.8137 - val_loss: 0.6558 - val_accuracy: 0.7572
Epoch 3/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.3730 - accuracy: 0.8631 - val_loss: 0.6795 - val_accuracy: 0.7652
Epoch 4/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.2382 - accuracy: 0.9150 - val_loss: 0.7859 - val_accuracy: 0.7481
Epoch 5/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.1206 - accuracy: 0.9596 - val_loss: 0.9447 - val_accuracy: 0.7505
Epoch 6/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0561 - accuracy: 0.9841 - val_loss: 1.1529 - val_accuracy: 0.7447
Epoch 7/30
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264/264 [==============================] - ETA: 0s - loss: 0.0273 - accuracy: 0.9930
264/264 [==============================] - 3s 12ms/step - loss: 0.0273 - accuracy: 0.9930 - val_loss: 1.4060 - val_accuracy: 0.7397
Epoch 8/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0164 - accuracy: 0.9965 - val_loss: 1.5316 - val_accuracy: 0.7426
Epoch 9/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0146 - accuracy: 0.9969 - val_loss: 1.6166 - val_accuracy: 0.7393
Epoch 10/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0128 - accuracy: 0.9972 - val_loss: 1.6368 - val_accuracy: 0.7356
Epoch 11/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0182 - accuracy: 0.9948 - val_loss: 1.7728 - val_accuracy: 0.7315
Epoch 12/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0198 - accuracy: 0.9938 - val_loss: 1.8126 - val_accuracy: 0.7349
Epoch 13/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0131 - accuracy: 0.9966 - val_loss: 1.8402 - val_accuracy: 0.7309
Epoch 14/30
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264/264 [==============================] - ETA: 0s - loss: 0.0145 - accuracy: 0.9958
264/264 [==============================] - 3s 12ms/step - loss: 0.0145 - accuracy: 0.9958 - val_loss: 1.8582 - val_accuracy: 0.7315
Epoch 15/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0154 - accuracy: 0.9951 - val_loss: 2.0422 - val_accuracy: 0.7306
Epoch 16/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0121 - accuracy: 0.9966 - val_loss: 1.9298 - val_accuracy: 0.7317
Epoch 17/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0139 - accuracy: 0.9964 - val_loss: 2.2133 - val_accuracy: 0.7256
Epoch 18/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0063 - accuracy: 0.9985 - val_loss: 2.0751 - val_accuracy: 0.7297
Epoch 19/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0090 - accuracy: 0.9982 - val_loss: 2.0989 - val_accuracy: 0.7338
Epoch 20/30
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264/264 [==============================] - ETA: 0s - loss: 0.0032 - accuracy: 0.9991
264/264 [==============================] - 3s 12ms/step - loss: 0.0032 - accuracy: 0.9991 - val_loss: 2.2916 - val_accuracy: 0.7412
Epoch 21/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 2.3868 - val_accuracy: 0.7391
Epoch 22/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0011 - accuracy: 0.9996 - val_loss: 2.4530 - val_accuracy: 0.7396
Epoch 23/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0105 - accuracy: 0.9968 - val_loss: 2.4064 - val_accuracy: 0.7298
Epoch 24/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0226 - accuracy: 0.9935 - val_loss: 2.0295 - val_accuracy: 0.7318
Epoch 25/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0170 - accuracy: 0.9951 - val_loss: 2.2087 - val_accuracy: 0.7222
Epoch 26/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0111 - accuracy: 0.9975 - val_loss: 2.2010 - val_accuracy: 0.7300
Epoch 27/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0050 - accuracy: 0.9991 - val_loss: 2.1097 - val_accuracy: 0.7349
Epoch 28/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0014 - accuracy: 0.9996 - val_loss: 2.3530 - val_accuracy: 0.7358
Epoch 29/30
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264/264 [==============================] - 3s 11ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 2.3927 - val_accuracy: 0.7380
Epoch 30/30
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264/264 [==============================] - 3s 11ms/step - loss: 0.0014 - accuracy: 0.9996 - val_loss: 2.4635 - val_accuracy: 0.7393
Run completed: runs/2023-04-06T23-55-56Z
Training run 4/9 (flags = list(64, 1e-04, 500, 100, "tanh"))
Using run directory runs/2023-04-06T23-57-31Z
> Flags1 <- list(
+ nodes = 128,
+ batch_size = 100,
+ activation = "relu",
+ learning_rate = 0.01,
+ epochs = 30
+ )
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = Flags1$nodes, activation = Flags1$activation) %>%
+ layer_dense(units = 32, activation = Flags1$activation) %>%
.... [TRUNCATED]
> model %>% compile(optimizer = optimizer_adam(lr = Flags1$learning_rate), loss = 'sparse_categorical_crossentropy', metrics = c('accuracy'))WARNING:absl:`lr` is deprecated, please use `learning_rate` instead, or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
> model %>% fit(
+ covid_train_dtm, train_la, epochs = Flags1$epochs, batch_size = Flags1$batch_size, validation_data=list(covid_val_dtm, valid_la)
.... [TRUNCATED]
Epoch 1/30
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264/264 [==============================] - 4s 14ms/step - loss: 0.7554 - accuracy: 0.6760 - val_loss: 0.6405 - val_accuracy: 0.7549
Epoch 2/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.4911 - accuracy: 0.8158 - val_loss: 0.6669 - val_accuracy: 0.7459
Epoch 3/30
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Epoch 4/30
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264/264 [==============================] - 3s 11ms/step - loss: 0.2183 - accuracy: 0.9230 - val_loss: 0.7779 - val_accuracy: 0.7575
Epoch 5/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.1071 - accuracy: 0.9658 - val_loss: 0.9761 - val_accuracy: 0.7508
Epoch 6/30
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264/264 [==============================] - 3s 13ms/step - loss: 0.0517 - accuracy: 0.9855 - val_loss: 1.2224 - val_accuracy: 0.7400
Epoch 7/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0259 - accuracy: 0.9945 - val_loss: 1.4019 - val_accuracy: 0.7482
Epoch 8/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0163 - accuracy: 0.9968 - val_loss: 1.5155 - val_accuracy: 0.7382
Epoch 9/30
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264/264 [==============================] - 3s 11ms/step - loss: 0.0112 - accuracy: 0.9979 - val_loss: 1.5768 - val_accuracy: 0.7406
Epoch 10/30
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264/264 [==============================] - ETA: 0s - loss: 0.0060 - accuracy: 0.9984
264/264 [==============================] - 3s 12ms/step - loss: 0.0060 - accuracy: 0.9984 - val_loss: 1.6849 - val_accuracy: 0.7393
Epoch 11/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0043 - accuracy: 0.9989 - val_loss: 1.7814 - val_accuracy: 0.7432
Epoch 12/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0042 - accuracy: 0.9990 - val_loss: 1.9248 - val_accuracy: 0.7377
Epoch 13/30
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264/264 [==============================] - ETA: 0s - loss: 0.0093 - accuracy: 0.9973
264/264 [==============================] - 3s 12ms/step - loss: 0.0093 - accuracy: 0.9973 - val_loss: 1.8679 - val_accuracy: 0.7312
Epoch 14/30
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264/264 [==============================] - ETA: 0s - loss: 0.0450 - accuracy: 0.9856
264/264 [==============================] - 3s 12ms/step - loss: 0.0450 - accuracy: 0.9856 - val_loss: 1.8342 - val_accuracy: 0.7335
Epoch 15/30
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264/264 [==============================] - 3s 13ms/step - loss: 0.0408 - accuracy: 0.9875 - val_loss: 1.7421 - val_accuracy: 0.7315
Epoch 16/30
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264/264 [==============================] - ETA: 0s - loss: 0.0222 - accuracy: 0.9935
264/264 [==============================] - 3s 13ms/step - loss: 0.0222 - accuracy: 0.9935 - val_loss: 1.9471 - val_accuracy: 0.7350
Epoch 17/30
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264/264 [==============================] - 3s 13ms/step - loss: 0.0145 - accuracy: 0.9959 - val_loss: 1.9346 - val_accuracy: 0.7349
Epoch 18/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0055 - accuracy: 0.9986 - val_loss: 2.1217 - val_accuracy: 0.7330
Epoch 19/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0022 - accuracy: 0.9994 - val_loss: 2.2006 - val_accuracy: 0.7355
Epoch 20/30
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264/264 [==============================] - 3s 13ms/step - loss: 0.0019 - accuracy: 0.9994 - val_loss: 2.2317 - val_accuracy: 0.7377
Epoch 21/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0019 - accuracy: 0.9994 - val_loss: 2.2514 - val_accuracy: 0.7352
Epoch 22/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0023 - accuracy: 0.9994 - val_loss: 2.2860 - val_accuracy: 0.7391
Epoch 23/30
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264/264 [==============================] - 4s 14ms/step - loss: 0.0044 - accuracy: 0.9992 - val_loss: 2.1800 - val_accuracy: 0.7386
Epoch 24/30
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264/264 [==============================] - 3s 13ms/step - loss: 0.0032 - accuracy: 0.9991 - val_loss: 2.3211 - val_accuracy: 0.7204
Epoch 25/30
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264/264 [==============================] - ETA: 0s - loss: 0.0206 - accuracy: 0.9938
264/264 [==============================] - 3s 13ms/step - loss: 0.0206 - accuracy: 0.9938 - val_loss: 2.2080 - val_accuracy: 0.7221
Epoch 26/30
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264/264 [==============================] - 3s 13ms/step - loss: 0.0176 - accuracy: 0.9949 - val_loss: 2.0944 - val_accuracy: 0.7257
Epoch 27/30
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264/264 [==============================] - ETA: 0s - loss: 0.0159 - accuracy: 0.9958
264/264 [==============================] - 3s 13ms/step - loss: 0.0159 - accuracy: 0.9958 - val_loss: 2.1564 - val_accuracy: 0.7200
Epoch 28/30
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264/264 [==============================] - 4s 13ms/step - loss: 0.0065 - accuracy: 0.9982 - val_loss: 2.3239 - val_accuracy: 0.7270
Epoch 29/30
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264/264 [==============================] - ETA: 0s - loss: 0.0021 - accuracy: 0.9992
264/264 [==============================] - 3s 13ms/step - loss: 0.0021 - accuracy: 0.9992 - val_loss: 2.5248 - val_accuracy: 0.7332
Epoch 30/30
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264/264 [==============================] - 3s 13ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 2.5931 - val_accuracy: 0.7332
Run completed: runs/2023-04-06T23-57-31Z
Training run 5/9 (flags = list(128, 1e-04, 100, 30, "tanh"))
Using run directory runs/2023-04-06T23-59-11Z
> Flags1 <- list(
+ nodes = 128,
+ batch_size = 100,
+ activation = "relu",
+ learning_rate = 0.01,
+ epochs = 30
+ )
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = Flags1$nodes, activation = Flags1$activation) %>%
+ layer_dense(units = 32, activation = Flags1$activation) %>%
.... [TRUNCATED]
> model %>% compile(optimizer = optimizer_adam(lr = Flags1$learning_rate), loss = 'sparse_categorical_crossentropy', metrics = c('accuracy'))WARNING:absl:`lr` is deprecated, please use `learning_rate` instead, or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
> model %>% fit(
+ covid_train_dtm, train_la, epochs = Flags1$epochs, batch_size = Flags1$batch_size, validation_data=list(covid_val_dtm, valid_la)
.... [TRUNCATED]
Epoch 1/30
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264/264 [==============================] - 5s 15ms/step - loss: 0.7601 - accuracy: 0.6726 - val_loss: 0.6720 - val_accuracy: 0.7374
Epoch 2/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.5000 - accuracy: 0.8150 - val_loss: 0.6567 - val_accuracy: 0.7550
Epoch 3/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.3723 - accuracy: 0.8631 - val_loss: 0.6907 - val_accuracy: 0.7648
Epoch 4/30
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264/264 [==============================] - 3s 13ms/step - loss: 0.2364 - accuracy: 0.9168 - val_loss: 0.7792 - val_accuracy: 0.7465
Epoch 5/30
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264/264 [==============================] - 3s 13ms/step - loss: 0.1173 - accuracy: 0.9622 - val_loss: 0.9280 - val_accuracy: 0.7514
Epoch 6/30
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264/264 [==============================] - 3s 13ms/step - loss: 0.0526 - accuracy: 0.9861 - val_loss: 1.1474 - val_accuracy: 0.7455
Epoch 7/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0259 - accuracy: 0.9941 - val_loss: 1.3158 - val_accuracy: 0.7488
Epoch 8/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0137 - accuracy: 0.9971 - val_loss: 1.4547 - val_accuracy: 0.7450
Epoch 9/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0084 - accuracy: 0.9984 - val_loss: 1.6544 - val_accuracy: 0.7402
Epoch 10/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0125 - accuracy: 0.9972 - val_loss: 1.6710 - val_accuracy: 0.7388
Epoch 11/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0118 - accuracy: 0.9968 - val_loss: 1.7909 - val_accuracy: 0.7397
Epoch 12/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0261 - accuracy: 0.9920 - val_loss: 1.7551 - val_accuracy: 0.7279
Epoch 13/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0276 - accuracy: 0.9918 - val_loss: 1.7767 - val_accuracy: 0.7321
Epoch 14/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0255 - accuracy: 0.9921 - val_loss: 1.8291 - val_accuracy: 0.7271
Epoch 15/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0138 - accuracy: 0.9964 - val_loss: 2.0036 - val_accuracy: 0.7277
Epoch 16/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0092 - accuracy: 0.9978 - val_loss: 1.9651 - val_accuracy: 0.7362
Epoch 17/30
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264/264 [==============================] - ETA: 0s - loss: 0.0053 - accuracy: 0.9989
264/264 [==============================] - 3s 12ms/step - loss: 0.0053 - accuracy: 0.9989 - val_loss: 1.9869 - val_accuracy: 0.7356
Epoch 18/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0027 - accuracy: 0.9992 - val_loss: 2.1416 - val_accuracy: 0.7426
Epoch 19/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0018 - accuracy: 0.9995 - val_loss: 2.2191 - val_accuracy: 0.7391
Epoch 20/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 2.3053 - val_accuracy: 0.7438
Epoch 21/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0020 - accuracy: 0.9994 - val_loss: 2.2908 - val_accuracy: 0.7309
Epoch 22/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0016 - accuracy: 0.9994 - val_loss: 2.3427 - val_accuracy: 0.7403
Epoch 23/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0146 - accuracy: 0.9954 - val_loss: 2.2258 - val_accuracy: 0.7367
Epoch 24/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0282 - accuracy: 0.9916 - val_loss: 2.0848 - val_accuracy: 0.7163
Epoch 25/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0145 - accuracy: 0.9955 - val_loss: 2.2609 - val_accuracy: 0.7268
Epoch 26/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0114 - accuracy: 0.9972 - val_loss: 2.2110 - val_accuracy: 0.7273
Epoch 27/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0031 - accuracy: 0.9991 - val_loss: 2.4558 - val_accuracy: 0.7358
Epoch 28/30
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Epoch 29/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0021 - accuracy: 0.9995 - val_loss: 2.4505 - val_accuracy: 0.7251
Epoch 30/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0013 - accuracy: 0.9995 - val_loss: 2.5399 - val_accuracy: 0.7263
Run completed: runs/2023-04-06T23-59-11Z
Training run 6/9 (flags = list(392, 0.05, 500, 100, "sigmoid"))
Using run directory runs/2023-04-07T00-00-51Z
> Flags1 <- list(
+ nodes = 128,
+ batch_size = 100,
+ activation = "relu",
+ learning_rate = 0.01,
+ epochs = 30
+ )
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = Flags1$nodes, activation = Flags1$activation) %>%
+ layer_dense(units = 32, activation = Flags1$activation) %>%
.... [TRUNCATED]
> model %>% compile(optimizer = optimizer_adam(lr = Flags1$learning_rate), loss = 'sparse_categorical_crossentropy', metrics = c('accuracy'))WARNING:absl:`lr` is deprecated, please use `learning_rate` instead, or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
> model %>% fit(
+ covid_train_dtm, train_la, epochs = Flags1$epochs, batch_size = Flags1$batch_size, validation_data=list(covid_val_dtm, valid_la)
.... [TRUNCATED]
Epoch 1/30
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264/264 [==============================] - 4s 14ms/step - loss: 0.7856 - accuracy: 0.6656 - val_loss: 0.6398 - val_accuracy: 0.7525
Epoch 2/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.5021 - accuracy: 0.8108 - val_loss: 0.6486 - val_accuracy: 0.7595
Epoch 3/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.3765 - accuracy: 0.8618 - val_loss: 0.6878 - val_accuracy: 0.7528
Epoch 4/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.2410 - accuracy: 0.9141 - val_loss: 0.7734 - val_accuracy: 0.7493
Epoch 5/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.1223 - accuracy: 0.9611 - val_loss: 0.9614 - val_accuracy: 0.7525
Epoch 6/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0523 - accuracy: 0.9856 - val_loss: 1.1565 - val_accuracy: 0.7482
Epoch 7/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0254 - accuracy: 0.9939 - val_loss: 1.3031 - val_accuracy: 0.7464
Epoch 8/30
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264/264 [==============================] - ETA: 0s - loss: 0.0188 - accuracy: 0.9964
264/264 [==============================] - 3s 12ms/step - loss: 0.0188 - accuracy: 0.9964 - val_loss: 1.4794 - val_accuracy: 0.7403
Epoch 9/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0131 - accuracy: 0.9973 - val_loss: 1.5544 - val_accuracy: 0.7406
Epoch 10/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0114 - accuracy: 0.9973 - val_loss: 1.6093 - val_accuracy: 0.7406
Epoch 11/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0112 - accuracy: 0.9975 - val_loss: 1.6891 - val_accuracy: 0.7405
Epoch 12/30
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264/264 [==============================] - ETA: 0s - loss: 0.0085 - accuracy: 0.9979
264/264 [==============================] - 3s 12ms/step - loss: 0.0085 - accuracy: 0.9979 - val_loss: 1.7261 - val_accuracy: 0.7443
Epoch 13/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0111 - accuracy: 0.9970 - val_loss: 1.8972 - val_accuracy: 0.7352
Epoch 14/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0267 - accuracy: 0.9916 - val_loss: 1.7603 - val_accuracy: 0.7333
Epoch 15/30
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264/264 [==============================] - ETA: 0s - loss: 0.0300 - accuracy: 0.9909
264/264 [==============================] - 3s 13ms/step - loss: 0.0300 - accuracy: 0.9909 - val_loss: 1.8518 - val_accuracy: 0.7271
Epoch 16/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0195 - accuracy: 0.9943 - val_loss: 1.9733 - val_accuracy: 0.7308
Epoch 17/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0090 - accuracy: 0.9979 - val_loss: 2.0211 - val_accuracy: 0.7355
Epoch 18/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0032 - accuracy: 0.9992 - val_loss: 2.0522 - val_accuracy: 0.7434
Epoch 19/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0057 - accuracy: 0.9985 - val_loss: 1.9920 - val_accuracy: 0.7376
Epoch 20/30
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264/264 [==============================] - ETA: 0s - loss: 0.0024 - accuracy: 0.9992
264/264 [==============================] - 3s 12ms/step - loss: 0.0024 - accuracy: 0.9992 - val_loss: 2.1797 - val_accuracy: 0.7418
Epoch 21/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0019 - accuracy: 0.9995 - val_loss: 2.1777 - val_accuracy: 0.7397
Epoch 22/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0042 - accuracy: 0.9987 - val_loss: 2.0923 - val_accuracy: 0.7383
Epoch 23/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0046 - accuracy: 0.9987 - val_loss: 2.3171 - val_accuracy: 0.7282
Epoch 24/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0125 - accuracy: 0.9962 - val_loss: 2.1467 - val_accuracy: 0.7262
Epoch 25/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0120 - accuracy: 0.9959 - val_loss: 2.2447 - val_accuracy: 0.7306
Epoch 26/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0083 - accuracy: 0.9977 - val_loss: 2.3482 - val_accuracy: 0.7383
Epoch 27/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0087 - accuracy: 0.9981 - val_loss: 2.2106 - val_accuracy: 0.7359
Epoch 28/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0029 - accuracy: 0.9992 - val_loss: 2.3488 - val_accuracy: 0.7396
Epoch 29/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 2.4517 - val_accuracy: 0.7376
Epoch 30/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0016 - accuracy: 0.9994 - val_loss: 2.5579 - val_accuracy: 0.7406
Run completed: runs/2023-04-07T00-00-51Z
Training run 7/9 (flags = list(64, 0.001, 1000, 30, "sigmoid"))
Using run directory runs/2023-04-07T00-02-28Z
> Flags1 <- list(
+ nodes = 128,
+ batch_size = 100,
+ activation = "relu",
+ learning_rate = 0.01,
+ epochs = 30
+ )
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = Flags1$nodes, activation = Flags1$activation) %>%
+ layer_dense(units = 32, activation = Flags1$activation) %>%
.... [TRUNCATED]
> model %>% compile(optimizer = optimizer_adam(lr = Flags1$learning_rate), loss = 'sparse_categorical_crossentropy', metrics = c('accuracy'))WARNING:absl:`lr` is deprecated, please use `learning_rate` instead, or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
> model %>% fit(
+ covid_train_dtm, train_la, epochs = Flags1$epochs, batch_size = Flags1$batch_size, validation_data=list(covid_val_dtm, valid_la)
.... [TRUNCATED]
Epoch 1/30
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264/264 [==============================] - 4s 14ms/step - loss: 0.8322 - accuracy: 0.6487 - val_loss: 0.6669 - val_accuracy: 0.7452
Epoch 2/30
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264/264 [==============================] - 3s 13ms/step - loss: 0.5310 - accuracy: 0.8036 - val_loss: 0.6531 - val_accuracy: 0.7593
Epoch 3/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.4107 - accuracy: 0.8513 - val_loss: 0.7182 - val_accuracy: 0.7408
Epoch 4/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.2836 - accuracy: 0.9005 - val_loss: 0.7432 - val_accuracy: 0.7543
Epoch 5/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.1578 - accuracy: 0.9480 - val_loss: 0.9195 - val_accuracy: 0.7468
Epoch 6/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0782 - accuracy: 0.9764 - val_loss: 1.0670 - val_accuracy: 0.7394
Epoch 7/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0370 - accuracy: 0.9905 - val_loss: 1.3200 - val_accuracy: 0.7385
Epoch 8/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0193 - accuracy: 0.9955 - val_loss: 1.4509 - val_accuracy: 0.7455
Epoch 9/30
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264/264 [==============================] - 3s 13ms/step - loss: 0.0101 - accuracy: 0.9978 - val_loss: 1.5680 - val_accuracy: 0.7406
Epoch 10/30
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264/264 [==============================] - ETA: 0s - loss: 0.0078 - accuracy: 0.9984
264/264 [==============================] - 3s 13ms/step - loss: 0.0078 - accuracy: 0.9984 - val_loss: 1.6642 - val_accuracy: 0.7423
Epoch 11/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0101 - accuracy: 0.9979 - val_loss: 1.7217 - val_accuracy: 0.7431
Epoch 12/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0138 - accuracy: 0.9967 - val_loss: 1.7751 - val_accuracy: 0.7304
Epoch 13/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0245 - accuracy: 0.9929 - val_loss: 1.6380 - val_accuracy: 0.7260
Epoch 14/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0226 - accuracy: 0.9935 - val_loss: 1.7023 - val_accuracy: 0.7374
Epoch 15/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0103 - accuracy: 0.9976 - val_loss: 2.0441 - val_accuracy: 0.7380
Epoch 16/30
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264/264 [==============================] - ETA: 0s - loss: 0.0065 - accuracy: 0.9986
264/264 [==============================] - 3s 12ms/step - loss: 0.0065 - accuracy: 0.9986 - val_loss: 1.9495 - val_accuracy: 0.7371
Epoch 17/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0046 - accuracy: 0.9990 - val_loss: 2.0418 - val_accuracy: 0.7374
Epoch 18/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0021 - accuracy: 0.9995 - val_loss: 2.2311 - val_accuracy: 0.7333
Epoch 19/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0022 - accuracy: 0.9995 - val_loss: 2.1730 - val_accuracy: 0.7408
Epoch 20/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0022 - accuracy: 0.9995 - val_loss: 2.1362 - val_accuracy: 0.7374
Epoch 21/30
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264/264 [==============================] - 3s 13ms/step - loss: 0.0015 - accuracy: 0.9994 - val_loss: 2.2580 - val_accuracy: 0.7418
Epoch 22/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0054 - accuracy: 0.9984 - val_loss: 2.0618 - val_accuracy: 0.7282
Epoch 23/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0275 - accuracy: 0.9926 - val_loss: 1.8182 - val_accuracy: 0.7207
Epoch 24/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0287 - accuracy: 0.9913 - val_loss: 2.0247 - val_accuracy: 0.7311
Epoch 25/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0114 - accuracy: 0.9967 - val_loss: 2.1284 - val_accuracy: 0.7301
Epoch 26/30
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264/264 [==============================] - ETA: 0s - loss: 0.0042 - accuracy: 0.9991
264/264 [==============================] - 3s 12ms/step - loss: 0.0042 - accuracy: 0.9991 - val_loss: 2.2389 - val_accuracy: 0.7345
Epoch 27/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0012 - accuracy: 0.9996 - val_loss: 2.3159 - val_accuracy: 0.7382
Epoch 28/30
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Epoch 29/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0011 - accuracy: 0.9996 - val_loss: 2.4114 - val_accuracy: 0.7382
Epoch 30/30
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264/264 [==============================] - 3s 13ms/step - loss: 0.0011 - accuracy: 0.9995 - val_loss: 2.4135 - val_accuracy: 0.7396
Run completed: runs/2023-04-07T00-02-28Z
Training run 8/9 (flags = list(64, 0.001, 200, 30, "tanh"))
Using run directory runs/2023-04-07T00-04-06Z
> Flags1 <- list(
+ nodes = 128,
+ batch_size = 100,
+ activation = "relu",
+ learning_rate = 0.01,
+ epochs = 30
+ )
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = Flags1$nodes, activation = Flags1$activation) %>%
+ layer_dense(units = 32, activation = Flags1$activation) %>%
.... [TRUNCATED]
> model %>% compile(optimizer = optimizer_adam(lr = Flags1$learning_rate), loss = 'sparse_categorical_crossentropy', metrics = c('accuracy'))WARNING:absl:`lr` is deprecated, please use `learning_rate` instead, or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
> model %>% fit(
+ covid_train_dtm, train_la, epochs = Flags1$epochs, batch_size = Flags1$batch_size, validation_data=list(covid_val_dtm, valid_la)
.... [TRUNCATED]
Epoch 1/30
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264/264 [==============================] - 4s 14ms/step - loss: 0.7815 - accuracy: 0.6653 - val_loss: 0.6638 - val_accuracy: 0.7456
Epoch 2/30
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Epoch 3/30
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264/264 [==============================] - 3s 13ms/step - loss: 0.3898 - accuracy: 0.8562 - val_loss: 0.6646 - val_accuracy: 0.7596
Epoch 4/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.2676 - accuracy: 0.9041 - val_loss: 0.7835 - val_accuracy: 0.7544
Epoch 5/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.1446 - accuracy: 0.9519 - val_loss: 0.9141 - val_accuracy: 0.7418
Epoch 6/30
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264/264 [==============================] - ETA: 0s - loss: 0.0669 - accuracy: 0.9809
264/264 [==============================] - 3s 12ms/step - loss: 0.0669 - accuracy: 0.9809 - val_loss: 1.0819 - val_accuracy: 0.7513
Epoch 7/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0315 - accuracy: 0.9929 - val_loss: 1.2630 - val_accuracy: 0.7437
Epoch 8/30
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264/264 [==============================] - ETA: 0s - loss: 0.0178 - accuracy: 0.9964
264/264 [==============================] - 3s 12ms/step - loss: 0.0178 - accuracy: 0.9964 - val_loss: 1.4445 - val_accuracy: 0.7444
Epoch 9/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0140 - accuracy: 0.9973 - val_loss: 1.5712 - val_accuracy: 0.7399
Epoch 10/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0099 - accuracy: 0.9979 - val_loss: 1.6499 - val_accuracy: 0.7391
Epoch 11/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0071 - accuracy: 0.9986 - val_loss: 1.8049 - val_accuracy: 0.7417
Epoch 12/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0047 - accuracy: 0.9990 - val_loss: 1.8565 - val_accuracy: 0.7376
Epoch 13/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0047 - accuracy: 0.9989 - val_loss: 2.0104 - val_accuracy: 0.7329
Epoch 14/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0122 - accuracy: 0.9964 - val_loss: 1.8798 - val_accuracy: 0.7241
Epoch 15/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0484 - accuracy: 0.9845 - val_loss: 1.8475 - val_accuracy: 0.7232
Epoch 16/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0251 - accuracy: 0.9931 - val_loss: 1.8742 - val_accuracy: 0.7248
Epoch 17/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0114 - accuracy: 0.9968 - val_loss: 1.9206 - val_accuracy: 0.7335
Epoch 18/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0042 - accuracy: 0.9989 - val_loss: 2.0287 - val_accuracy: 0.7358
Epoch 19/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0044 - accuracy: 0.9990 - val_loss: 2.0775 - val_accuracy: 0.7371
Epoch 20/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0018 - accuracy: 0.9996 - val_loss: 2.1672 - val_accuracy: 0.7390
Epoch 21/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 2.1407 - val_accuracy: 0.7394
Epoch 22/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0016 - accuracy: 0.9996 - val_loss: 2.2364 - val_accuracy: 0.7365
Epoch 23/30
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264/264 [==============================] - ETA: 0s - loss: 0.0019 - accuracy: 0.9995
264/264 [==============================] - 3s 12ms/step - loss: 0.0019 - accuracy: 0.9995 - val_loss: 2.2489 - val_accuracy: 0.7367
Epoch 24/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0021 - accuracy: 0.9994 - val_loss: 2.3194 - val_accuracy: 0.7373
Epoch 25/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0032 - accuracy: 0.9992 - val_loss: 2.2945 - val_accuracy: 0.7304
Epoch 26/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0302 - accuracy: 0.9910 - val_loss: 2.0574 - val_accuracy: 0.7215
Epoch 27/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0245 - accuracy: 0.9936 - val_loss: 2.2060 - val_accuracy: 0.7244
Epoch 28/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0087 - accuracy: 0.9978 - val_loss: 2.2638 - val_accuracy: 0.7241
Epoch 29/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0033 - accuracy: 0.9993 - val_loss: 2.3288 - val_accuracy: 0.7256
Epoch 30/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0025 - accuracy: 0.9995 - val_loss: 2.3702 - val_accuracy: 0.7341
Run completed: runs/2023-04-07T00-04-06Z
Training run 9/9 (flags = list(64, 0.01, 1000, 50, "relu"))
Using run directory runs/2023-04-07T00-05-43Z
> Flags1 <- list(
+ nodes = 128,
+ batch_size = 100,
+ activation = "relu",
+ learning_rate = 0.01,
+ epochs = 30
+ )
> model = keras_model_sequential()
> model %>%
+ layer_dense(units = Flags1$nodes, activation = Flags1$activation) %>%
+ layer_dense(units = 32, activation = Flags1$activation) %>%
.... [TRUNCATED]
> model %>% compile(optimizer = optimizer_adam(lr = Flags1$learning_rate), loss = 'sparse_categorical_crossentropy', metrics = c('accuracy'))WARNING:absl:`lr` is deprecated, please use `learning_rate` instead, or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.
> model %>% fit(
+ covid_train_dtm, train_la, epochs = Flags1$epochs, batch_size = Flags1$batch_size, validation_data=list(covid_val_dtm, valid_la)
.... [TRUNCATED]
Epoch 1/30
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264/264 [==============================] - 4s 14ms/step - loss: 0.8093 - accuracy: 0.6580 - val_loss: 0.6504 - val_accuracy: 0.7437
Epoch 2/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.5096 - accuracy: 0.8104 - val_loss: 0.6514 - val_accuracy: 0.7547
Epoch 3/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.3705 - accuracy: 0.8631 - val_loss: 0.6729 - val_accuracy: 0.7608
Epoch 4/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.2380 - accuracy: 0.9158 - val_loss: 0.7779 - val_accuracy: 0.7567
Epoch 5/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.1212 - accuracy: 0.9610 - val_loss: 0.9727 - val_accuracy: 0.7494
Epoch 6/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0525 - accuracy: 0.9864 - val_loss: 1.1911 - val_accuracy: 0.7443
Epoch 7/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0230 - accuracy: 0.9948 - val_loss: 1.3333 - val_accuracy: 0.7479
Epoch 8/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0131 - accuracy: 0.9975 - val_loss: 1.4511 - val_accuracy: 0.7505
Epoch 9/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0094 - accuracy: 0.9983 - val_loss: 1.4953 - val_accuracy: 0.7470
Epoch 10/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0109 - accuracy: 0.9977 - val_loss: 1.5232 - val_accuracy: 0.7514
Epoch 11/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0082 - accuracy: 0.9979 - val_loss: 1.6208 - val_accuracy: 0.7479
Epoch 12/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0171 - accuracy: 0.9953 - val_loss: 1.7870 - val_accuracy: 0.7426
Epoch 13/30
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264/264 [==============================] - ETA: 0s - loss: 0.0316 - accuracy: 0.9905
264/264 [==============================] - 3s 12ms/step - loss: 0.0316 - accuracy: 0.9905 - val_loss: 1.8545 - val_accuracy: 0.7298
Epoch 14/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0378 - accuracy: 0.9871 - val_loss: 1.6854 - val_accuracy: 0.7321
Epoch 15/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0156 - accuracy: 0.9958 - val_loss: 1.9835 - val_accuracy: 0.7148
Epoch 16/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0083 - accuracy: 0.9976 - val_loss: 1.9806 - val_accuracy: 0.7396
Epoch 17/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0039 - accuracy: 0.9991 - val_loss: 2.0409 - val_accuracy: 0.7388
Epoch 18/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0029 - accuracy: 0.9993 - val_loss: 2.1208 - val_accuracy: 0.7453
Epoch 19/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0014 - accuracy: 0.9996 - val_loss: 2.1669 - val_accuracy: 0.7447
Epoch 20/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0013 - accuracy: 0.9995 - val_loss: 2.1824 - val_accuracy: 0.7352
Epoch 21/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 2.1875 - val_accuracy: 0.7485
Epoch 22/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 2.2945 - val_accuracy: 0.7411
Epoch 23/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 2.2980 - val_accuracy: 0.7464
Epoch 24/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 2.3371 - val_accuracy: 0.7420
Epoch 25/30
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264/264 [==============================] - ETA: 0s - loss: 0.0014 - accuracy: 0.9995
264/264 [==============================] - 3s 12ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 2.3679 - val_accuracy: 0.7400
Epoch 26/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0159 - accuracy: 0.9946 - val_loss: 2.0138 - val_accuracy: 0.7270
Epoch 27/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0424 - accuracy: 0.9880 - val_loss: 1.9509 - val_accuracy: 0.7277
Epoch 28/30
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Epoch 29/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0090 - accuracy: 0.9978 - val_loss: 2.1169 - val_accuracy: 0.7283
Epoch 30/30
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264/264 [==============================] - 3s 12ms/step - loss: 0.0033 - accuracy: 0.9990 - val_loss: 2.3600 - val_accuracy: 0.7344
Run completed: runs/2023-04-07T00-05-43ZrunsData frame: 9 x 20 # ... with 15 more columns:
# epochs, epochs_completed, metrics, model, loss_function,
# optimizer, learning_rate, script, start, end, completed, output,
# source_code, context, type2.1) 9th run highest accuracy with 0.9995
view_run(runs$run_dir[9])Warning: argument 'compressed' is ignored for the internal methodWarning: incomplete final line found on 'C:\Users\bbopp3\AppData\Local\Temp\RtmpIl9haY\file5e4876b849bc/source/bikes.R'Warning: incomplete final line found on 'C:\Users\bbopp3\AppData\Local\Temp\RtmpIl9haY\file5e4876b849bc/source/bikess.R'2.3) YES, validation_loss stop decreasing after several epochs. it stop at 8th.
Q3. (5 pts)Now that we tuned the hyperparameters and selected the best model, we don’t need to withhold validation data anymore and can use it for training. Add the validation data to the train data. You can do this by first, converting your covid_train_dtm and covid_val_dtm into matrices and then combining them using “rbind”. Make sure that you also combine the train and validation labels (sentiments). Now re-train your best model on this new training data and evaluate it on the test data. Compute precision and recall for the positive/neutral/ and negative classes as you did for assignment 2. How does this model perform compare to your naïve Bayes model in assignment 2?
data1 = (as.matrix(covid_train_dtm))
data2 = (as.matrix(covid_val_dtm))
train_data = rbind(data1, data2)
lab_tuned = covid_dataset[1:32925, ]$Sentiment
model = keras_model_sequential()
model %>%
layer_dense(units = 128, activation = 'sigmoid') %>%
layer_dense(units = 128, activation = 'sigmoid') %>%
layer_dense(units = 3, activation = "softmax")
model %>% compile(
optimizer = optimizer_adam(learning_rate = 0.0001),
loss = 'sparse_categorical_crossentropy',
metrics = c('accuracy'))
set.seed(123)
model %>% fit(
train_data, lab_tuned , epochs = 30, batch_size = 500,
validation_data=list(covid_test_dtm, test_la))Epoch 1/30
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66/66 [==============================] - ETA: 0s - loss: 1.0787 - accuracy: 0.4125
66/66 [==============================] - 3s 37ms/step - loss: 1.0787 - accuracy: 0.4125 - val_loss: 1.0240 - val_accuracy: 0.4356
Epoch 2/30
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66/66 [==============================] - 2s 29ms/step - loss: 1.0134 - accuracy: 0.4495 - val_loss: 1.0007 - val_accuracy: 0.4563
Epoch 3/30
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66/66 [==============================] - 2s 27ms/step - loss: 0.9812 - accuracy: 0.5336 - val_loss: 0.9617 - val_accuracy: 0.5664
Epoch 4/30
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66/66 [==============================] - ETA: 0s - loss: 0.9253 - accuracy: 0.6192
66/66 [==============================] - 2s 27ms/step - loss: 0.9253 - accuracy: 0.6192 - val_loss: 0.8992 - val_accuracy: 0.6376
Epoch 5/30
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66/66 [==============================] - 2s 28ms/step - loss: 0.8509 - accuracy: 0.6675 - val_loss: 0.8332 - val_accuracy: 0.6647
Epoch 6/30
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66/66 [==============================] - ETA: 0s - loss: 0.7799 - accuracy: 0.7021
66/66 [==============================] - 2s 27ms/step - loss: 0.7799 - accuracy: 0.7021 - val_loss: 0.7781 - val_accuracy: 0.6936
Epoch 7/30
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66/66 [==============================] - 2s 27ms/step - loss: 0.7176 - accuracy: 0.7360 - val_loss: 0.7297 - val_accuracy: 0.7232
Epoch 8/30
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66/66 [==============================] - ETA: 0s - loss: 0.6640 - accuracy: 0.7652
66/66 [==============================] - 2s 25ms/step - loss: 0.6640 - accuracy: 0.7652 - val_loss: 0.6916 - val_accuracy: 0.7386
Epoch 9/30
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66/66 [==============================] - 2s 24ms/step - loss: 0.6189 - accuracy: 0.7882 - val_loss: 0.6591 - val_accuracy: 0.7518
Epoch 10/30
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66/66 [==============================] - 2s 23ms/step - loss: 0.5809 - accuracy: 0.8029 - val_loss: 0.6360 - val_accuracy: 0.7651
Epoch 11/30
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66/66 [==============================] - 2s 23ms/step - loss: 0.5490 - accuracy: 0.8160 - val_loss: 0.6147 - val_accuracy: 0.7713
Epoch 12/30
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66/66 [==============================] - 2s 27ms/step - loss: 0.5216 - accuracy: 0.8274 - val_loss: 0.5994 - val_accuracy: 0.7760
Epoch 13/30
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66/66 [==============================] - 2s 28ms/step - loss: 0.4989 - accuracy: 0.8349 - val_loss: 0.5876 - val_accuracy: 0.7798
Epoch 14/30
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66/66 [==============================] - 2s 27ms/step - loss: 0.4795 - accuracy: 0.8423 - val_loss: 0.5788 - val_accuracy: 0.7815
Epoch 15/30
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66/66 [==============================] - 2s 27ms/step - loss: 0.4622 - accuracy: 0.8472 - val_loss: 0.5721 - val_accuracy: 0.7845
Epoch 16/30
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66/66 [==============================] - ETA: 0s - loss: 0.4478 - accuracy: 0.8529
66/66 [==============================] - 2s 27ms/step - loss: 0.4478 - accuracy: 0.8529 - val_loss: 0.5676 - val_accuracy: 0.7851
Epoch 17/30
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66/66 [==============================] - ETA: 0s - loss: 0.4361 - accuracy: 0.8564
66/66 [==============================] - 2s 27ms/step - loss: 0.4361 - accuracy: 0.8564 - val_loss: 0.5645 - val_accuracy: 0.7858
Epoch 18/30
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66/66 [==============================] - 2s 27ms/step - loss: 0.4242 - accuracy: 0.8604 - val_loss: 0.5627 - val_accuracy: 0.7875
Epoch 19/30
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66/66 [==============================] - 2s 29ms/step - loss: 0.4144 - accuracy: 0.8637 - val_loss: 0.5631 - val_accuracy: 0.7878
Epoch 20/30
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66/66 [==============================] - 2s 27ms/step - loss: 0.4059 - accuracy: 0.8668 - val_loss: 0.5626 - val_accuracy: 0.7889
Epoch 21/30
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66/66 [==============================] - ETA: 0s - loss: 0.3982 - accuracy: 0.8691
66/66 [==============================] - 2s 27ms/step - loss: 0.3982 - accuracy: 0.8691 - val_loss: 0.5626 - val_accuracy: 0.7892
Epoch 22/30
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66/66 [==============================] - ETA: 0s - loss: 0.3914 - accuracy: 0.8707
66/66 [==============================] - 2s 26ms/step - loss: 0.3914 - accuracy: 0.8707 - val_loss: 0.5642 - val_accuracy: 0.7885
Epoch 23/30
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66/66 [==============================] - 2s 27ms/step - loss: 0.3850 - accuracy: 0.8733 - val_loss: 0.5660 - val_accuracy: 0.7897
Epoch 24/30
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66/66 [==============================] - 2s 26ms/step - loss: 0.3793 - accuracy: 0.8751 - val_loss: 0.5691 - val_accuracy: 0.7895
Epoch 25/30
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66/66 [==============================] - 2s 26ms/step - loss: 0.3743 - accuracy: 0.8764 - val_loss: 0.5705 - val_accuracy: 0.7891
Epoch 26/30
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66/66 [==============================] - 2s 27ms/step - loss: 0.3693 - accuracy: 0.8789 - val_loss: 0.5736 - val_accuracy: 0.7879
Epoch 27/30
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66/66 [==============================] - ETA: 0s - loss: 0.3648 - accuracy: 0.8800
66/66 [==============================] - 2s 25ms/step - loss: 0.3648 - accuracy: 0.8800 - val_loss: 0.5758 - val_accuracy: 0.7886
Epoch 28/30
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66/66 [==============================] - 1s 23ms/step - loss: 0.3610 - accuracy: 0.8814 - val_loss: 0.5790 - val_accuracy: 0.7896
Epoch 29/30
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66/66 [==============================] - 2s 23ms/step - loss: 0.3574 - accuracy: 0.8832 - val_loss: 0.5823 - val_accuracy: 0.7884
Epoch 30/30
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66/66 [==============================] - 2s 24ms/step - loss: 0.3545 - accuracy: 0.8832 - val_loss: 0.5864 - val_accuracy: 0.7879model %>% evaluate(covid_test_dtm, test_la)
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258/258 [==============================] - 1s 2ms/step - loss: 0.5864 - accuracy: 0.7879
loss accuracy
0.5864105 0.7879009 predicted_labels = as.numeric(model %>% predict(covid_test_dtm) %>%k_argmax())
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258/258 [==============================] - 1s 2ms/steppredicted_labels[1:30] [1] 0 0 0 0 0 1 0 1 2 0 0 1 2 0 0 1 1 0 1 0 0 1 1 1 0 2 0 1 0 1t = table(test_la, predicted_labels)
t predicted_labels
test_la 0 1 2
0 2978 370 237
1 400 2447 275
2 263 201 1061error = (t[1,2]+t[2,1])/sum(t)
error[1] 0.09353741the code provided is 0.09353741, which means that the error rate is approximately 9.35%.
The accuracy rate of the Naive Bayes model in the assignment was found to be 67.3%. In contrast, the accuracy rate achieved through hyper-tuning of the Artificial Neural Network (ANN) was around 90%.